CAREER GUIDE · ROLE
AI Platform Engineer: Responsibilities, Skills and Production Evidence
What an AI platform engineer owns, how the role differs from MLOps and DevOps, and what evidence proves platform capability.
An AI platform engineer builds and operates the shared, self-service platform that AI teams consume. The role owns the platform capabilities — model registry, training pipelines, serving, evaluation, observability, governance, cost control — as a product with golden paths, documentation and adoption metrics. It differs from MLOps (which operates specific models) and DevOps (which operates general infrastructure) by its AI-specific scope and platform-as-product mindset. The evidence of capability is a platform capability map, golden-path walkthroughs, and adoption data — not just infrastructure scripts.
What Does an AI Platform Engineer Do?
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Which Systems Does an AI Platform Engineer Own?
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How Does the Role Differ From MLOps, DevOps, SRE and AI Engineering?
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AI Platform Engineer vs MLOps vs DevOps vs SRE
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Which Software and Infrastructure Skills Are Required?
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How Much Machine Learning Knowledge Is Required?
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Which Artefacts Demonstrate Platform Capability?
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Which Projects Belong in an AI Platform Portfolio?
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How Should Someone Prepare for This Role?
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Evidence Matrix — Responsibility × Evidence Artefact
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Key AI Platform Engineer Concepts
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AI Platform Engineer Role & Project Matcher
Conceptual visualization — not a live computation.
You have the role definition and the evidence matrix. The AIOps Course trains you to build each platform capability with hands-on projects — from the model registry through the golden path to the adoption dashboard.
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Sources and Evidence
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- Tier 1